---
tags:
- autotrain
language: en
widget:
- text: I am still waiting on my card?
datasets:
- banking77
model-index:
- name: BERT-Banking77
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: BANKING77
      type: banking77
    metrics:
    - name: Accuracy
      type: accuracy
      value: 92.64
    - name: Macro F1
      type: macro-f1
      value: 92.64
    - name: Weighted F1
      type: weighted-f1
      value: 92.6
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: banking77
      type: banking77
      config: default
      split: test
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9275974025974026
      verified: true
    - name: Precision Macro
      type: precision
      value: 0.9304852147882035
      verified: true
    - name: Precision Micro
      type: precision
      value: 0.9275974025974026
      verified: true
    - name: Precision Weighted
      type: precision
      value: 0.9304852147882035
      verified: true
    - name: Recall Macro
      type: recall
      value: 0.9275974025974028
      verified: true
    - name: Recall Micro
      type: recall
      value: 0.9275974025974026
      verified: true
    - name: Recall Weighted
      type: recall
      value: 0.9275974025974026
      verified: true
    - name: F1 Macro
      type: f1
      value: 0.9276213811661831
      verified: true
    - name: F1 Micro
      type: f1
      value: 0.9275974025974026
      verified: true
    - name: F1 Weighted
      type: f1
      value: 0.927621381166183
      verified: true
    - name: loss
      type: loss
      value: 0.31997689604759216
      verified: true
co2_eq_emissions: 0.03330651014155927
---
#  `BERT-Banking77` Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 940131041
- CO2 Emissions (in grams): 0.03330651014155927

## Validation Metrics

- Loss: 0.3505457043647766
- Accuracy: 0.9263261296660118
- Macro F1: 0.9268371013605569
- Micro F1: 0.9263261296660118
- Weighted F1: 0.9259954221865809
- Macro Precision: 0.9305746406646502
- Micro Precision: 0.9263261296660118
- Weighted Precision: 0.929031563971418
- Macro Recall: 0.9263724620088746
- Micro Recall: 0.9263261296660118
- Weighted Recall: 0.9263261296660118


## Usage

You can use cURL to access this model:

```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/philschmid/autotrain-does-it-work-940131041
```

Or Python API:

```
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_id = 'philschmid/BERT-Banking77'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)
classifier = pipeline('text-classification', tokenizer=tokenizer, model=model)
classifier('What is the base of the exchange rates?')
```